1 PRISAD: A Partitioned Rendering Infrastructure for Scalable Accordion Drawing James Slack,...

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PRISAD: A Partitioned Rendering Infrastructure for Scalable Accordion Drawing

James Slack, Kristian Hildebrand, Tamara Munzner

University of British ColumbiaImager

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Accordion Drawing (AD)• Rubber-sheet navigation

– Stretch part of surface, the rest squishes– Borders nailed down– A Focus+Context technique

[Robertson et al 1991][Sarkar et al 1993]

• Guaranteed visibility– Marks always visible– Important for scalability

[Munzner et al 2003]

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PRITree

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PRITree: InfoVis Contest Dataset

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PRISeq

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Guaranteed Visibility

• Marks are always visible• Easy with small datasets

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Guaranteed Visibility Challenges

• Hard with larger datasets• A mark could be invisible

– Outside the window• Solution: constrained navigation

– Underneath other marks• Solution: avoid 3D

– Smaller than a pixel• Solution: smart culling

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Guaranteed Visibility: Small Items

• Naïve culling may not draw all marked items

GV no GV

Guaranteed visibilityof marks

No guaranteed visibility

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Guaranteed Visibility: Small Items

• Naïve culling may not draw all marked items

GV no GV

Guaranteed visibilityof marks

No guaranteed visibility

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Related Work

• TreeJuxtaposer– Side-by-side tree comparison– First AD application– Up to 500k tree nodes

[Munzner et al. 2003]

• SequenceJuxtaposer– DNA sequence comparison– Pre-aligned A,C,G,T data– Up to 2M nucleotides

[Slack et al. 2004]

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Related Work

• TJC/TJC-Q– Scalability

• Up to 15 million node trees• Rendering in under 0.3 seconds• TJC: hardware supported picking

– Limitations• Non-generic: AD inseparable from tree• Poor performance on bushy trees• No support for multiple-tree comparison

[Beermann et al. 2005]

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Goals of PRISAD

• Generic AD infrastructure– Tree and sequence applications

• PRITree is TreeJuxtaposer using PRISAD• PRISeq is SequenceJuxtaposer using PRISAD

• Efficiency– Faster rendering: minimize overdrawing– Smaller memory footprint

• Correctness– Rendering with no gaps: eliminate overculling

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PRISAD Navigation

• Generic navigation infrastructure– Application independent– Uses deformable grid– Split lines

• Grid lines define object boundaries

– Horizontal and vertical separate• Independently movable• As in TJC

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Split line hierarchy

• Data structure supports navigation, picking, drawing• Two interpretations

– Linear ordering

– Hierarchical subdivision

A B C D E F

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PRISAD Architecture

World-space discretization• Preprocessing

• Initializing data structures• Placing geometry

Screen-space rendering• Frame updating

• Analyzing navigation state• Drawing geometry

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World-space Discretization

Interplay between infrastructure and application

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• Application-specific layout of dataset– Non-overlapping objects

• Initialize PRISAD split line hierarchies– Objects aligned by split lines

Laying Out & Initializing

A A C C

A T T T68

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34 5

97

4

5

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Gridding• Each geometric object assigned its four

encompassing split line boundaries

A A C C

A T T T

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Mapping

• PRITree mapping initializes leaf references– Bidirectional O(1) reference between leaves and

split lines

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468

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973

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12

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84

95

63

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21 Map

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Split line Leaf index

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Screen-space RenderingControl flow to draw each frame

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Partitioning

• Partition object set into bite-sized ranges– Using current split line screen-space positions

• Required for every frame

– Subdivision stops if region smaller than 1 pixel• Or if range contains only 1 object

1234

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[1,2]

[3,4]

[5]

{ [1,2], [3,4], [5] }

Queue of ranges

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Seeding

• Reordering range queue result from partition– Marked regions get priority in queue

• Drawn first to provide landmarks

1234

5

[1,2]

[3,4]

[5]

{ [1,2], [3,4], [5] }

{ [3,4], [5], [1,2] }

Ordered queue

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Drawing Single Range

• Each enqueued object range drawn according to application geometry– Selection for trees– Aggregation for sequences

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PRITree Range Drawing

• Select suitable leaf in each range

• Draw path from leaf to the root– Ascent-based tree drawing

– Efficiency: minimize overdrawing • Only draw one path per range

1234

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[3,4]{ [3,4], [5], [1,2] }

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Rendering Dense Regions– Correctness: eliminate overculling

• Bad leaf choices would result in misleading gaps

– Efficiency: maximize partition size to reduce rendering• Too much reduction would result in gaps

Intended rendering Partition size too big

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Rendering Dense Regions– Correctness: eliminate overculling

• Bad leaf choices would result in misleading gaps

– Efficiency: maximize partition size to reduce rendering• Too much reduction would result in gaps

Intended rendering Partition size too big

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PRITree Skeleton• Guaranteed visibility of marked subtrees during

progressive rendering

First frame: one path per marked group

Full scene: entire marked subtrees

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PRISeq Range Drawing: Aggregation

• Aggregate range to select box color for each sequence– Random select to break ties

A A C C

A T T T

[1,4]

A

T

[1,4]

T T T C T

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PRISeq Range Drawing• Collect identical nucleotides in column

– Form single box to represent identical objects• Attach to split line hierarchy cache• Lazy evaluation

• Draw vertical column

A

T

T

T

A

{ A:[1,1], T:[2,3] }

1

2

3

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PRISAD Performance

• PRITree vs. TreeJuxtaposer (TJ)

• Synthetic and real datasets

– Complete binary trees• Lowest branching factor• Regular structure

– Star trees• Highest possible branching factor

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InfoVis Contest Benchmarks• Two 190k node trees• Directly compare TJ and PT

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OpenDirectory benchmarks• Two 480k node trees• Too large for TJ

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PRITree Rendering Time PerformanceTreeJuxtaposer renders all nodes for star trees

• Branching factor k leads to O(k) performanceInfoVis 2003 Contest dataset• 5x rendering speedupA closer look at the fastest rendering times

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PRITree handles 4 million nodes in under 0.4 seconds• TreeJuxtaposer takes twice as long to render 1 million nodes

Detailed Rendering Time PerformanceTreeJuxtaposer valley from overculling

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Memory Performance1GB difference for InfoVis contest comparison

• Marked range storage changes improve scalabilityLinear memory usage for both applications

• 4-5x more efficient for synthetic datasets

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Performance Comparison

• PRITree vs. TreeJuxtaposer– Detailed benchmarks against identical TJ functionality

• 5x faster, 8x smaller footprint

• PRITree vs. TJC/TJC-Q– TJC on exotic HW: 4x smaller, 2x faster– TJC-Q on commodity HW: same size, no speed given– Both mostly tested on balanced binary trees

• Unlimited overdrawing possible in bushy areas vs. PT bound

• PRISeq vs. SequenceJuxtaposer (SJ)– 15x rendering speedup– 20x improvement in memory footprint

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Future Work

• Future work– Editing and annotating datasets– PRISAD support for application specific actions

• Logging, replay, undo, other user actions

– Develop process or template for building applications

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Conclusions

• PRISAD infrastructure for efficient, correct, and generic accordion drawing

• Efficient and correct rendering – Screen-space partitioning tightly bounds overdrawing and

eliminates overculling

• First generic AD infrastructure– PRITree renders 5x faster than TJ– PRISeq renders 20x larger datasets than SJ

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More Information

• Software, papers, videos– http://olduvai.sourceforge.net

• PRITree and PRISeq• Requires Java, OpenGL